Built-in

Handle errors and exceptions in Python using 'try' and 'except' statement, with optional 'else', 'finally' blocks, use 'raise' to throw exceptions, assign exception to variable with 'as' keyword, handle multiple types of exceptions.

Learn how to assign default value to function parameters by using '=' in function signature, default parameters must be at end of the parameter list

Learn how to use the built-in logging module in Python to keep track of events in your program and output messages to a log file or designated logging service. #Python #Logging

In Python, the yield keyword allows functions to produce a series of values over time, rather than computing them all at once, making it efficient for large data sets or for producing a series of values that can be used in a for loop.

Learn how to perform various statistics on a Pandas DataFrame such as mean, median, mode, standard deviation, variance, minimum and maximum, sum, count and more using built-in methods like .mean(), .median(), .std(), .min(), .max(), .sum(), .count(), .describe() etc.

Learn how to easily read and manipulate data from excel, csv and json formats in Python using popular libraries such as pandas, openpyxl, and json

To install a Python library such as Pandas, use pip or conda package manager by typing "pip install [library_name]" or "conda install [library_name]" and then import it in your script to start using it.

Creating a custom library in Python involves creating a new directory, adding an __init__.py file, creating modules with related functions/classes, importing and exposing desired functions/classes in __init__.py, and importing the library in other scripts.

A well-structured Python script uses functions, meaningful variable and function names, follows PEP8, has docstrings, and uses consistent indentation and formatting to make the code easy to understand and maintain.